• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1133-1142
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• 2023

A rapid increase in power capacity in a power system can seriously reduce system reliability by causing the fault capacity to exceed the breaking capacity of circuit breaker. Fault current limiter is a practical and effective way to improve reliability by limiting fault capacity to the breaking capacity level. In this study, in order to help develop an application methodology when applying fault current limiters to power systems, first the topology and operating principles of each type of fault current limiters was reviewed, and the main advantages and disadvantages was compared. Next, to verify the effect of applying fault current limiter to the power system, the power system in which the fault current limiter was introduced was modeled. Finally, after simulating a three-phase short-circuit fault using EMTP-RV, the effect of application was verified by comparing the fault current before and after application of the fault current limiter and confirming that the fault current was reduced by the fault current limiter.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1600-1609
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• 2016

As the penetration of different types of renewable energy sources (RES) and energy storage systems (ESS) increases, the importance of stability in AC microgrid is being emphasized. Especially, RES and ESS which are operated using power electronics have difference in output characteristics according to control structures. When faults like single-line-to-ground fault or islanding operation occur, this means that a fault should be interpreted in different way. Therefore, it is necessary to analyze fault characteristics in AC microgrid in case of grid-connected mode and standalone mode. In this paper, the fault analysis for AC microgrid is carried out using PSCAD/EMTDC and an overvoltage problem and the countermeasures were proposed.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1876-1877
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• 2011

In this paper, a winding fault diagnosis method base on MCSA(Motor Current Signature Analysis) for BLDC motor is proposed. This method is programmed by LabVIEW for winding fault diagnosis. For winding fault diagnosis, two types of winding fault(shorted turn at one pole, shorted turn at two pole in same phase) are put intentionally in on phase. The motor current is collected by hole sensor, and transformed by the Park's transform, and then the Park's Vector Pattern are obtained, Usually this pattern is formed an ellipse, so a proper threshold value of distortion ratio(the ratio of the shortest axis and longest axis of ellipse) is suggested for winding faults diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.8
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• pp.410-417
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• 2005

This paper analyses the transient phenomena against single line to ground fault and lightning surge on underground power cable systems. For analysis in various fault conditions, several actual underground power cable systems are modeled using ATP In ground fault, the transient characteristic of the conductor and the sheath according to the fault current and the installation types of CCPU are analysed. In lightning surge strokes, the various unbalanced conditions including the length of crossbonded lead, the breakdown of CCPU and distance unbalance are considered. This paper is expected to contribute the establishment of proper protection methods against transients on underground power cable systems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.459-462
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• 2000

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device operating. so it is well hon that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents detections in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. Wavelet transform analysis is applied the time-scale information to fault signal. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.3
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• pp.125-131
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.193-196
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• 1997

This paper presents the Fault Pattern Estimator(FPE) using the neural network for the protection of the T/L. The proposed FPE has two neural network parts of the fault-types classification and the fault-location estimation. It can detect the fault signals more Quickly and accurately. To prove the performance of the FPE, we have tested using a relaying signals obtained from the EMTP simulations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.